



import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''image_processor''', '''tokenizer''']
			A      		=	'''FlavaImageProcessor'''
			A      		=	('''BertTokenizer''', '''BertTokenizerFast''')

			def __init__(self,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=None,   **SCREAMING_SNAKE_CASE_					)       ->   Dict:
						UpperCAmelCase_:		List[Any]							      =     None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead.""",   SCREAMING_SNAKE_CASE_,   )
									UpperCAmelCase_:		Union[str, Any]							      =     kwargs.pop("""feature_extractor"""					)

						UpperCAmelCase_:		str							      =     image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
									raise ValueError("""You need to specify an `image_processor`."""					)
						if tokenizer is None:
									raise ValueError("""You need to specify a `tokenizer`."""					)

						super().__init__(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     self.image_processor

			def __call__(self,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = 0,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   List[str]:

						if text is None and images is None:
									raise ValueError("""You have to specify either text or images. Both cannot be none."""					)

						if text is not None:
									UpperCAmelCase_:		Any							      =     self.tokenizer(
									    text=SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_,   padding=SCREAMING_SNAKE_CASE_,   truncation=SCREAMING_SNAKE_CASE_,   max_length=SCREAMING_SNAKE_CASE_,   stride=SCREAMING_SNAKE_CASE_,   pad_to_multiple_of=SCREAMING_SNAKE_CASE_,   return_token_type_ids=SCREAMING_SNAKE_CASE_,   return_attention_mask=SCREAMING_SNAKE_CASE_,   return_overflowing_tokens=SCREAMING_SNAKE_CASE_,   return_special_tokens_mask=SCREAMING_SNAKE_CASE_,   return_offsets_mapping=SCREAMING_SNAKE_CASE_,   return_length=SCREAMING_SNAKE_CASE_,   verbose=SCREAMING_SNAKE_CASE_,   return_tensors=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_,   )
						if images is not None:
									UpperCAmelCase_:		Tuple							      =     self.image_processor(
									    SCREAMING_SNAKE_CASE_,   return_image_mask=SCREAMING_SNAKE_CASE_,   return_codebook_pixels=SCREAMING_SNAKE_CASE_,   return_tensors=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_,   )

						if text is not None and images is not None:
									encoding.update(SCREAMING_SNAKE_CASE_					)
									return encoding
						elif text is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_					),   tensor_type=SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   *SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)       ->   Union[str, Any]:
						return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   *SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)       ->   Union[str, Any]:
						return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			@property
			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		Dict							      =     self.tokenizer.model_input_names
						UpperCAmelCase_:		List[Any]							      =     self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)

			@property
			def      __snake_case       (self					)       ->   Tuple:
						warnings.warn(
						    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""",   SCREAMING_SNAKE_CASE_,   )
						return self.image_processor_class

			@property
			def      __snake_case       (self					)       ->   List[Any]:
						warnings.warn(
						    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""",   SCREAMING_SNAKE_CASE_,   )
						return self.image_processor











import numpy as np







def  lowerCAmelCase_	(lowerCAmelCase__:		np.ndarray		,						lowerCAmelCase__:		float  ):





			"""simple docstring"""


			return np.where(vector > 0		,						lowerCAmelCase__		,						(alpha * (np.exp(lowerCAmelCase__  ) - 1))  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






